Supertagging for Domain Adaptation: An Approach with Law Texts

Abstract

In this paper, we present a German supertagger that analy- ses syntactic functions in linear order. We apply a statistical sequential model, conditional random fields (CRF), to Swiss law texts, in a real world scenario in which the training data of the domain is missing. We show that the small amount of in-domain training data that was informed by linguistic hard and soft constraints and domain constraints achieved a label accuracy of 90% in the domain data, thus outperforming state-of-the-art parsers.